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首页> 外文会议>Proceedings of the 2011 14th IEEE International Multitopic Conference >Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment
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Comparing SVM and naïve Bayes classifiers for text categorization with Wikitology as knowledge enrichment

机译：比较用于文本分类的SVM和朴素贝叶斯分类器与知识丰富的Wikitology

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摘要

The activity of labeling of documents according to their content is known as text categorization. Many experiments have been carried out to enhance text categorization by adding background knowledge to the document using knowledge repositories like Word Net, Open Project Directory (OPD), Wikipedia and Wikitology. In our previous work, we have carried out intensive experiments by extracting knowledge from Wikitology and evaluating the experiment on Support Vector Machine with 10- fold cross-validations. The results clearly indicate Wikitology is far better than other knowledge bases. In this paper we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers under text enrichment through Wikitology. We validated results with 10-fold cross validation and shown that NB gives an improvement of +28.78%, on the other hand SVM gives an improvement of +636% when compared with baseline results. Naïve Bayes classifier is better choice when external enriching is used through any external knowledge base.
机译：根据文档内容标记文档的活动称为文本分类。通过使用诸如Word Net，Open Project Directory（OPD），Wikipedia和Wikitology之类的知识库将背景知识添加到文档中，已经进行了许多实验来增强文本分类。在我们以前的工作中，我们通过从Wikitology中提取知识并在支持向量机上进行10倍交叉验证来评估实验，从而进行了密集的实验。结果清楚地表明，Wikitology远胜于其他知识库。在本文中，我们正在比较通过Wikitology进行文本丰富化的支持向量机（SVM）和朴素贝叶斯（NB）分类器。我们通过10倍交叉验证对结果进行了验证，结果表明NB与基线结果相比，改善了+ 28.78％，另一方面，SVM对+改善了+ 636％。当通过任何外部知识库使用外部扩展时，朴素贝叶斯分类器是更好的选择。

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